The University of the Philippines recently opened a doctorate degree in the National School of Engineering for Artificial Intelligence at their Diliman campus. According to the university's official press release, they created the course in hopes of "developing graduates who had the mindset to expand the field of artificial intelligence". We have the command bot Alexa, our iPhones have Siri, and some laptops have Cortana. There's even the new Google Home that can control our air-conditioner and lights! All those names we just mentioned are forms of Artificial Intelligence. It takes a lot of programming to create these bots.

We introduce a new dataset for conversational question answering over Knowledge Graphs (KGs) with verbalized answers. Question answering over KGs is currently focused on answer generation for single-turn questions (KGQA) or multiple-tun conversational question answering (ConvQA). However, in a real-world scenario (e.g., voice assistants such as Siri, Alexa, and Google Assistant), users prefer verbalized answers. This paper contributes to the state-of-the-art by extending an existing ConvQA dataset with multiple paraphrased verbalized answers. We perform experiments with five sequence-to-sequence models on generating answer responses while maintaining grammatical correctness. We additionally perform an error analysis that details the rates of models' mispredictions in specified categories. Our proposed dataset extended with answer verbalization is publicly available with detailed documentation on its usage for wider utility.

Have you ever dreamed of being your own boss or being able to work from anywhere in the world? I have a secret for you. This is possible if you become a virtual assistant. Virtual assistants are no longer just online secretaries. Employers are looking for virtual assistants that can update websites, create email newsletters, schedule social media posts and more.

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Digital transformation is no longer an inward tactic used to reform an organisation's operations; it's now a necessary undertaking sought out by CIOs and IT leaders. Recent developments have pushed organisations to embrace digitisation, causing the fourth industrial revolution and technologies such as artificial intelligence to go mainstream. Even though, according to Gartner, only 53 per cent of AI projects make it from prototype into production, companies can't ignore the benefits of successful AI implementation. Enhanced AI solutions such as the artificial intelligence of things (AIoT), conversational AI and machine learning (ML) are improving the future of digital transformation and offer more innovative ways than ever before to address business challenges. Today's AI solutions can be customised to address a company's unique set of challenges.

Amazon's Echo Show 15 comes with a digital photo frame picture that enables it to display only photos or artwork, uninterrupted by random Alexa skill suggestions, recipes or your schedule. Only the 15.6-inch had that feature, though -- until now. According to The Verge, the e-commerce giant has recently added its dedicated photo frame feature to all Echo Show Models in the US, the UK, Canada, Germany, France, Italy, Spain and Australia. The Verge says you can activate the slideshow by saying the voice command: "Alexa, start Photo Frame." Your smart display will then start a slideshow using the contents of your Amazon Photos and your Facebook account. It can also display a random selection of stock images if you've yet to upload your personal photos or have yet to link your accounts with the device.

Over a decade ago, online dating existed through sites such as match.com But they did not tend to attract younger users, and for many there was a stigma attached to using them. But apps such as Grindr, which launched in 2009, and Tinder, which launched in September 2012, have radically changed the landscape of online dating. Emily Witt, the author of Future Sex, a book that examines sex in the internet age, tells Nosheen Iqbal about the origin story of Tinder, meeting the founders, and why it was such an instant success. Emily discusses her own experience of using it and the apps that followed in its wake. The apps have grown in response to a culture where people are single for a lot longer, and also where there's a changing sexual morality that's open to different kinds of relationships, Emily believes.

Speaking about the evolution of technology you can't skip mentioning artificial intelligence simply because in our day-to-day activities we do interact with the technology mostly even without knowing that we do. If you own a smartphone, laptop, smartwatches, desktop, and so many devices yes you do interact with artificial intelligence or use it to accomplish some of your tasks such as google search, Camera, meeting platforms like zoom, Google spreadsheets, Microsoft Cortana, Apple Siri, Google Assistant, Google map, Apple map, Google lens, social media interaction, etc. The scope of artificial intelligence has expanded and evolved over time. So, it is time to think about how you can leverage this technology to improve the revenue of your business in this article I will highlight the potential of conversational artificial intelligence for businesses. Conversational AI involves three concepts artificial intelligence, human language, and automation.

Embedding learning is an important technique in deep recommendation models to map categorical features to dense vectors. However, the embedding tables often demand an extremely large number of parameters, which become the storage and efficiency bottlenecks. Distributed training solutions have been adopted to partition the embedding tables into multiple devices. However, the embedding tables can easily lead to imbalances if not carefully partitioned. This is a significant design challenge of distributed systems named embedding table sharding, i.e., how we should partition the embedding tables to balance the costs across devices, which is a non-trivial task because 1) it is hard to efficiently and precisely measure the cost, and 2) the partition problem is known to be NP-hard. In this work, we introduce our novel practice in Meta, namely AutoShard, which uses a neural cost model to directly predict the multi-table costs and leverages deep reinforcement learning to solve the partition problem. Experimental results on an open-sourced large-scale synthetic dataset and Meta's production dataset demonstrate the superiority of AutoShard over the heuristics. Moreover, the learned policy of AutoShard can transfer to sharding tasks with various numbers of tables and different ratios of the unseen tables without any fine-tuning. Furthermore, AutoShard can efficiently shard hundreds of tables in seconds. The effectiveness, transferability, and efficiency of AutoShard make it desirable for production use. Our algorithms have been deployed in Meta production environment. A prototype is available at https://github.com/daochenzha/autoshard

Machine learning or commonly known as ML is a type of artificial intelligence or artificial intelligence that can help software work more accurately in predicting results without being explicitly programmed to do a job. Machine learning algorithms use historical data as input data to predict new output values. One example of the application of machine learning is recommendation machines on e-commerce platforms or social media platforms. Other applications of machine learning that are currently being widely adopted are fraud detection, spam filtering, malware threat detection, business process automation, and Predictive maintenance. Today machine learning is very important because it gives companies insight into customer behavior trends and business operational patterns and supports the development of new products.